Rod Acosta, Kevin Furbish, Ibrahim Khan, Anthony Washington
Methods
What is a neural Network?
A neural network is a type of algorithm that mimics the structure and function of the human brain. Their goal is to create an artificial system that can process and analyze data in a similar way.
There are different types of neural networks but there are some common elements between most of them. Those elements are:
Artificial Neurons
Layers
Neural Network Layers
Neural networks usually have three types of layers:
Input Layer
Hidden layers
Output layer
What are embeddings?
Embeddings are a technique that allow us to map words or phrases into a corresponding vector of real numbers, where the position and direction of the vector capture the word’s semantic meaning in relation to other words.
They make high-dimensional data like words readable to our algorithm/model and allows our model to recognize and learn meaningful relationships and similarities between words
Dense Layer & Cosine Similarity
Cosine Similarity
Measures the cosine of the angle between two non-zero vectors, providing a measure of similarity.
The smaller the angle the higher the similarity between the two vectors.
A logistic regression model with a sigmoid activation function used for binary classification.
It outputs the probability that the input belongs to a positive class.
\(y=\sigma(W⋅z+b)\)
Where:
z is the flattened input vector.
W is the weight vector.
b is the bias term.
\(\sigma(x) = \frac{1}{1+e^{-x}}\) is the sigmoid function.
Sentiment Analysis
Through the use of a neural network and it’s hidden layers (embedding & dense), and the cosine similarity we are able to take inputs and classify them as being part of a positive or negative class based on what our model has learned from our training dataset.